The securities markets produce a vast amount of data. These data record the price history of individual securities in these markets. Statistical arbitrage attempts to take advantage of this vast amount of data to analyze trends and other relationships in the price of securities and employ trading strategies that take advantage of these trends and relationships. The term statistical arbitrage describes a broad family of trading strategies that seek to profit from historically observed relationships between stocks or other securities.
A common type of statistical arbitrage works by trading correlated stock pairs against each other. This type of statistical arbitrage works on the principle that, if one stock in the correlated pair moves significantly in price without a comparable move in the other, the spread in the price of this stock pair will revert to its usual relationship. That is, either the underperforming stock will rise, or the outperforming stock will sell off to return the stocks to their appropriate relative valuation, or “spread.” The statistical arbitrage trading model shorts the outperforming stock and buys the weak name to profit if the price spread returns to its normal level. The trigger for trading on the stock pair comes from historical price data only. By contrast, a typical long/short pair reflects an investor's belief in changing relative values for two stocks in a sector, while a risk (merger) arbitrage pairs trade relies on the expectation of an upcoming event to move the spread. These approaches have used a small number of pairs to trade.
Other types of statistical arbitrage besides mean-reverting pairs exist, such as technical, or “black box,” trading systems, which also attempt to spot recurring trading patterns and trade around them, generating gains when historical price patterns reassert themselves. Whatever the underlying pattern sought, the expected edge in a statistical arbitrage trade is small.
One of the biggest problems in statistical arbitrage of any sort is the risk of “data mining,” that is, the risk that, if you manipulate the data enough, it will support any premise. Data mining occurs when a supposed relationship is “discovered” in the historical data that is actually just the result of chance. Given enough data or enough flexibility in what one calls a pattern, the probability is very high that something interesting will be found. But this “discovery” does not necessarily mean that the relationship has any true meaning, or, more importantly, that the pattern will recur in the future.
In view of the foregoing, there is a need for systems and methods for analyzing historical performance of financial securities and identifying trades in those securities based on the securities' current position as compared to this historical performance.